We propose a novel deep learning architecture for regressing disparity from a rectified pair
of stereo images. We leverage knowledge of the problem's geometry to form a cost volume …

We present a method for extracting depth information from a rectified image pair. Our
approach focuses on the first stage of many stereo algorithms: the matching cost …

In the past year, convolutional neural networks have been shown to perform extremely well
for stereo estimation. However, current architectures rely on siamese networks which exploit …

We present a method for extracting depth information from a rectified image pair. We train a
convolutional neural network to predict how well two image patches match and use it to …

Low-precision floating-point arithmetic is a powerful tool for accelerating scientific computing
applications, especially those in artificial intelligence. Here, we present an investigation …

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in
computer vision and image understanding, with respect to the modeling, the inference and …

In recent years, convolutional neural networks have shown its strong power for stereo
matching cost learning. Current approaches learn the parameters of their models from public …

Software reliability engineering is focused on engineering techniques for develo** and
maintaining software systems whose reliability can be quantitatively evaluated. In order to …

Handbook of deep learning applications Smart Innovation, Systems and Technologies 136
Valentina Emilia Balas Sanjiban Sekhar Roy Dharmendra Sharma Pijush Samui Editors …

While machine learning has been instrumental to the ongoing progress in most areas of
computer vision, it has not been applied to the problem of stereo matching with similar …